The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs

• URL: http://arxiv.org/abs/2407.18786v1
• Date: Fri, 26 Jul 2024 14:47:31 GMT
• Title: The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs
• Authors: Aleix Sant, Carlos Escolano, Audrey Mash, Francesca De Luca Fornaciari, Maite Melero,
• Abstract summary: This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs) Four widely-used test sets are employed to benchmark various base LLMs, comparing their translation quality and gender bias against state-of-the-art Neural Machine Translation (NMT) models for English to Catalan (En $rightarrow$ Ca) and English to Spanish (En $rightarrow$ Es) Our findings reveal pervasive gender bias across all models, with base LLMs exhibiting a higher degree of bias compared to NMT models.
• Score: 1.707677607445317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs). Four widely-used test sets are employed to benchmark various base LLMs, comparing their translation quality and gender bias against state-of-the-art Neural Machine Translation (NMT) models for English to Catalan (En $\rightarrow$ Ca) and English to Spanish (En $\rightarrow$ Es) translation directions. Our findings reveal pervasive gender bias across all models, with base LLMs exhibiting a higher degree of bias compared to NMT models. To combat this bias, we explore prompting engineering techniques applied to an instruction-tuned LLM. We identify a prompt structure that significantly reduces gender bias by up to 12% on the WinoMT evaluation dataset compared to more straightforward prompts. These results significantly reduce the gender bias accuracy gap between LLMs and traditional NMT systems.

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